Objective:

The central mission of the Southern Center on Environmentally-Driven Disparities in Birth Outcomes is to determine how environmental, social, and host factors jointly contribute to health disparities. Specific aims of the Center are:

To develop and operate an interdisciplinary children’s health research center with a focus on understanding how biological, physiological, environmental, and social aspects of vulnerability contribute to health disparities;

To enhance research in children’s health at Duke by promoting research interactions among programs in biomedicine, pediatric and obstetric care, environmental health, and the social sciences and establishing an infrastructure to support and extend interdisciplinary research;

To develop new methodologies for incorporating innovative statistical analysis into children’s environmental health research and policy practice, with a particular emphasis on spatial, genetic and proteomic analysis;

To serve as a technical and educational resource to the local community, region, the nation, and to international agencies in the area of children’s health and health disparities; and,

To translate the results of the Center into direct interventions in clinical care and practice.

The central objective of the Healthy Pregnancy, Healthy Baby Study is to determine how the interaction of environmental, social, and host factors contributes to disparities in birth outcomes between African-American and white women in the American South. There are four specific aims:

b. Determine whether these clusters are more or less present in African-American versus white populations and quantify the proportion of health disparities explained by differences in cluster frequency, and

c. Identify environmental, social, and host factors that cluster to predict birth outcomes within the African-American and white sub-samples and compare these clusters across racial groups.

Progress Summary:

As of 4/1/2011, 1889 women have been enrolled in the study. Demographic data indicate that we are successfully recruiting women who are most at risk for adverse pregnancy outcomes, particularly low-income, low educational attainment, and non-Hispanic black women.

We have been highly successful in collection of participant-level data as well as biological samples, with greater than 90% attainment of maternal blood sample for genetic and environmental analyses. Collection of cord blood and placental samples, which began in June 2007, has also been successful with approximately 944 delivery samples collected.

All maternal data is georeferenced (i.e., linked to the physical address of the mother) using Geographic Information System (GIS) software. The Healthy Pregnancy/Healthy Baby Study also includes an in-depth neighborhood assessment designed to capture both built environment and community-level social stressors and community resources. The cohort study and neighborhood assessment data are spatially linked to extensive environmental and demographic data at a highly resolved spatial scale.

Genetic Data and Analysis. To date, we have generated genotypes on approximately 1600 blood samples from pregnant women. We have genotyped 412 Single Nucleotide Polymorphisms (SNPs) in fifty-two genes.

Psychosocial Indicators. Analyses have been completed on psychosocial influences on birth outcomes. The relationships among pregnancy intention, psychosocial health, and pregnancy outcomes have been examined, with a paper accepted. In addition, we are examining pregnancy intention, behavioral choice, and environmental exposures. The influences of psychosocial health and smoking status have been studied, and a paper has been submitted. In order to reduce the number of psychosocial variables, cluster analysis has been performed, resulting in three distinct clusters of women. Cluster analysis on the personality indices were also performed. A resulting paper presented at the Society of Behavioral Medicine meeting reported that women with an adverse personality profile were more likely to express several psychosocial risk factors (e.g., increased depressive symptoms, increased unwanted pregnancy) and had a six-fold higher rate of preterm (<32 weeks) delivery than women with a resilient profile.

Maternal Medical Complications. Fetal health is not only individually determined, but is also influenced by maternal health and well-being. This past year, we have begun to examine maternal outcomes, as well. In particular, we have begun to focus on hypertensive disorders during pregnancy.

Statistical Methods Development. We developed several new statistical methodologies designed to improve analysis of the Project B data, as well as to advance statistical analysis more broadly. First, we developed and implemented methods for finding important predictors in quantile regression when there are a very large number of covariates. These methods adapted the lasso and elastic net penalties for quantile regression. We applied the methods on a mid-study sample of women to uncover a previously unreported interaction: women who smoke and who have high blood lead levels tend to have babies with lower birth weights.

Second, we developed and implemented methods for using factor analysis models in the context of quantile regression. The investigative team believes that many of the predictors can be grouped into underlying factors. For example, the Project B data contain several variables that measure maternal stress, and arguably we should connect birth outcomes to the underlying factor of stress rather than its individual indicators. As another example, the data contain several imperfect indicators of smoking status, and we would like to connect birth outcomes to the underyling factor of true smoking status. We implemented the model on a mid-study sample of women from Project B, and we found that the smoking factor was a strong predictor of low birth weight.

Third, we developed and implemented methods for accounting for mid-study changes in measurement scales. These methods were needed because the Project B investigators switched assay labs for measuring blood levels of heavy metals midway through data collection in order to take advantage of finer measurement scales. Exploratory analysis indicated that the distributions of levels for several exposures were markedly different across the labs, so that analyses based on a simple concatenation of the two labs’ data would be biased. Using the second lab scale as the standard, so that effectively measurements before the lab switch are treated as missing, we developed general purpose methodology for imputing plausible values of the missing exposure measurements. The methods are based on assumptions about the relative ranks of measurements in the two scales, e.g., a measurement in the 10th percentile in one scale should be at the 10th percentile in the other scale. We implemented this methodology on the Project B data to provide the investigative team with improved data product.

We also developed a Bayesian growth mixture model to jointly examine the associations between longitudinal blood pressure measurements, preterm birth (PTB), and low birthweight (LBW). The model partitions women into distinct classes characterized by a mean arterial pressure (MAP) curve and joint probabilities of PTB and LBW. Each class contains a unique mixed effects model for MAP with class-specific regression coefficients and random effect covariances. To account for the high correlation between PTB and LBW, we introduce a bivariate probit model within each class to capture residual within-class dependence between PTB and LBW. The model permits the association between PTB and LBW to vary by class, so that for some classes, PTB and LBW may be positively correlated, while for others, they may be uncorrelated or negatively correlated. We also allow maternal covariates to influence the class probabilities via a multinomial logit model. For posterior computation, we propose an efficient Markov chain Monte Carlo algorithm that combines full-conditional Gibbs and Metropolis steps. We apply our model to a sample of 1027 women enrolled in the Healthy Pregnancy, Healthy Baby Study, a prospective cohort study of host, social, and environmental contributors to disparities in pregnancy outcomes.

Future Activities:

In the next year, we will focus on data analysis and further statistical methods innovation. Our primary interest is in bringing these two pieces together. The statistical methods innovation is driven by the needs of our data analysis and thus will continue to explore means to reduce the dimensionality of the genetic and other data, as well as impute missing data. Our overall goal is to identify complex interactions amongst the three sides of the triangle we hypothesize influence pregnancy outcomes: host, social, and environmental contributors.

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.